Network based multifactorial modelling of miRNA-target interactions.
Ontology highlight
ABSTRACT:
SUBMITTER: Ari Yuka S
PROVIDER: S-EPMC7983860 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundMicroRNAs (miRNAs) have been associated with the Hirschsprung disease (HSCR) pathogenesis, however, the findings are still inconclusive. We aimed to investigate the effect of miRNA-206 and its targets, fibronectin 1 (FN1), serum deprivation response (SDPR), and paired box 3 (PAX3) expressions on multifactorial HSCR in Indonesia, a genetically distinct group within Asia.MethodsWe determined the miRNA-206, FN1, SDPR and PAX3 expressions in both the ganglionic and aganglionic colon of HSCR patients and control colon by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsTwenty-one sporadic HSCR patients and thirteen controls were ascertained in this study. The miRNA-206 expression was up-regulated (2-fold) in the ganglionic colon and down-regulated (0.5-fold) in the aganglionic colon compared to the control group (ΔCT 12.4 ± 3.0 vs. 14.1 ± 3.9 vs. 13.1 ± 2.7), but these differences did not reach significant levels (p = 0.48 and p = 0.46, respectively). Interestingly, the FN1 expression was significantly increased in both the ganglionic (38-fold) and aganglionic colon (18-fold) groups compared to the control group ΔCT 5.7 ± 3.0 vs. 6.8 ± 2.3 vs. 11.0 ± 5.0; p = 0.001 and p = 0.038, respectively). Furthermore, the expressions of SDPR were similar in the ganglionic, aganglionic and control colon groups (ΔCT 2.4 ± 0.6 vs. 2.2 ± 0.4 vs. 2.1 ± 0.6; p = 0.16 and p = 0.39, respectively), while no change was observed in the PAX3 expression between the ganglionic, aganglionic, and control colon groups (ΔCT 3.8 ± 0.8 vs. 4.1 ± 0.8 vs. 3.7 ± 1.1; p = 0.83 and p = 0.44, respectively).ConclusionOur study is the first report of aberrant FN1 expressions in the colon of patients with HSCR and supplies further insights into the contribution of aberrant FN1 expression in the HSCR pathogenesis.
| S-EPMC6323865 | biostudies-literature
Project description:In this work, we performed an analysis of the networks of interactions between drugs and their targets to assess how connected the compounds are. For our purpose, the interactions were downloaded from the DrugBank database, and we considered all drugs approved by the FDA. Based on topological analysis of this interaction network, we obtained information on degree, clustering coefficient, connected components, and centrality of these interactions. We identified that this drug-target interaction network cannot be divided into two disjoint and independent sets, i.e., it is not bipartite. In addition, the connectivity or associations between every pair of nodes identified that the drug-target network is constituted of 165 connected components, where one giant component contains 4376 interactions that represent 89.99% of all the elements. In this regard, the histamine H1 receptor, which belongs to the family of rhodopsin-like G-protein-coupled receptors and is activated by the biogenic amine histamine, was found to be the most important node in the centrality of input-degrees. In the case of centrality of output-degrees, fostamatinib was found to be the most important node, as this drug interacts with 300 different targets, including arachidonate 5-lipoxygenase or ALOX5, expressed on cells primarily involved in regulation of immune responses. The top 10 hubs interacted with 33% of the target genes. Fostamatinib stands out because it is used for the treatment of chronic immune thrombocytopenia in adults. Finally, 187 highly connected sets of nodes, structured in communities, were also identified. Indeed, the largest communities have more than 400 elements and are related to metabolic diseases, psychiatric disorders and cancer. Our results demonstrate the possibilities to explore these compounds and their targets to improve drug repositioning and contend against emergent diseases.
| S-EPMC7968663 | biostudies-literature
Project description:Identification of drug-target interactions (DTIs) has great practical importance in the drug discovery process for known diseases. However, only a small proportion of DTIs in these databases has been verified experimentally, and the computational methods for predicting the interactions remain challenging. As a result, some effective computational models have become increasingly popular for predicting DTIs. In this work, the authors predict potential DTIs from the local structure of drug-target associations' network, which is different from the traditional global network similarity methods based on structure and ligand. A novel method called PPDTS is proposed to predict DTIs. First, according to the DTIs' network local structure, the known DTIs are converted into a binary network. Second, the Resource Allocation algorithm is used to obtain a drug-drug similarity network and a target-target similarity network. Third, a Collaborative Filtering algorithm is used with the known drug-target topology information to obtain similarity scores. Fourth, the linear combination of drug-target similarity model and the target-drug similarity model are innovatively proposed to obtain the final prediction results. Finally, the experimental performance of PPDTS has proved to be higher than that of the previously mentioned four popular network-based similarity methods, which is validated in different experimental datasets. Some of the predicted results can be supported in UniProt and DrugBank databases.
| S-EPMC8849239 | biostudies-literature
Project description:In a previous microarray study, we identified a subset of micro RNAS (miRNAs), which expression was distinctly higher in atretic than healthy follicles of cattle. In the present study, we investigated the involvement of those miRNAs in granulosa and theca cells during atresia. Reverse Transcription-quantitative Polymerase Chain Reaction (RT-qPCR) confirmed that miR-21-5p/-3p, miR-150, miR-409a, miR-142-5p, miR-378, miR-222, miR-155, and miR-199a-5p were expressed at higher levels in atretic than healthy follicles (9-17 mm, classified based on steroidogenic capacity). All miRNAs except miR-21-3p and miR-378 were expressed at higher levels in theca than granulosa cells. The expression of 13 predicted miRNA targets was determined in follicular cells by RT-qPCR, revealing downregulation of HIF1A, ETS1, JAG1, VEGFA, and MSH2 in either or both cell types during atresia. Based on increases in miRNA levels simultaneous with decreases in target levels in follicular cells, several predicted miRNA target interactions were confirmed that are putatively involved in follicular atresia, namely miR-199a-5p/miR-155-HIF1A in granulosa cells, miR-155/miR-222-ETS1 in theca cells, miR-199a-5p-JAG1 in theca cells, miR-199a-5p/miR-150/miR-378-VEGFA in granulosa and theca cells, and miR-155-MSH2 in theca cells. These results offer novel insight on the involvement of miRNAs in follicle development by identifying a miRNA target network that is putatively involved in follicle atresia.
| S-EPMC5145806 | biostudies-other
Project description:BackgroundIn the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and links are different. In order to take into account the heterogeneity of the semantic network, meta-path-based topological patterns were investigated for link prediction.ResultsSupervised machine learning models were constructed based on meta-path topological features of an enriched semantic network, which was derived from Chem2Bio2RDF, and was expanded by adding compound and protein similarity neighboring links obtained from the PubChem databases. The additional semantic links significantly improved the predictive performance of the supervised learning models. The binary classification model built upon the enriched feature space using the Random Forest algorithm significantly outperformed an existing semantic link prediction algorithm, Semantic Link Association Prediction (SLAP), to predict unknown links between compounds and protein targets in an evolving network. In addition to link prediction, Random Forest also has an intrinsic feature ranking algorithm, which can be used to select the important topological features that contribute to link prediction.ConclusionsThe proposed framework has been demonstrated as a powerful alternative to SLAP in order to predict DTIs using the semantic network that integrates chemical, pharmacological, genomic, biological, functional, and biomedical information into a unified framework. It offers the flexibility to enrich the feature space by using different normalization processes on the topological features, and it can perform model construction and feature selection at the same time.
| S-EPMC4830032 | biostudies-literature
Project description:BackgroundThere is a growing body of evidence associating microRNAs (miRNAs) with human diseases. MiRNAs are new key players in the disease paradigm demonstrating roles in several human diseases. The functional association between miRNAs and diseases remains largely unclear and far from complete. With the advent of high-throughput functional genomics techniques that infer genes and biological pathways dysregulted in diseases, it is now possible to infer functional association between diseases and biological molecules by integrating disparate biological information.ResultsHere, we first used Lasso regression model to identify miRNAs associated with disease signature as a proof of concept. Then we proposed an integrated approach that uses disease-gene associations from microarray experiments and text mining, and miRNA-gene association from computational predictions and protein networks to build functional associations network between miRNAs and diseases. The findings of the proposed model were validated against gold standard datasets using ROC analysis and results were promising (AUC=0.81). Our protein network-based approach discovered 19 new functional associations between prostate cancer and miRNAs. The new 19 associations were validated using miRNA expression data and clinical profiles and showed to act as diagnostic and prognostic prostate biomarkers. The proposed integrated approach allowed us to reconstruct functional associations between miRNAs and human diseases and uncovered functional roles of newly discovered miRNAs.ConclusionsLasso regression was used to find associations between diseases and miRNAs using their gene signature. Defining miRNA gene signature by integrating the downstream effect of miRNAs demonstrated better performance than the miRNA signature alone. Integrating biological networks and multiple data to define miRNA and disease gene signature demonstrated high performance to uncover new functional associations between miRNAs and diseases.
| S-EPMC3606436 | biostudies-literature
Project description:MicroRNA (miRNA) plays an important role in the degradation and inhibition of mRNAs and is a kind of essential drug targets for cancer therapy. To facilitate the clinical cancer research, we proposed a network-based strategy to identify the cancer-related miRNAs and to predict their targeted genes based on the gene expression profiles. The strategy was validated by using the data sets of acute myeloid leukemia (AML), breast invasive carcinoma (BRCA), and kidney renal clear cell carcinoma (KIRC). The results showed that in the top 20 miRNAs ranked by their degrees, 90.0% (18/20), 70.0% (14/20), and 70.0% (14/20) miRNAs were found to be associated with the cancers for AML, BRCA, and KIRC, respectively. The KEGG pathways and GO terms enriched with the genes that were predicted as the targets of the cancer-related miRNAs were significantly associated with the biological processes of cancers. In addition, several genes, which were predicted to be regulated by more than three miRNAs, were identified to be the potential drug targets annotated by using the human protein atlas database. Our results demonstrated that the proposed strategy can be helpful for predicting the miRNA-mRNA interactions in tumorigenesis and identifying the cancer-related miRNAs as the potential drug targets.
| S-EPMC5558674 | biostudies-literature
Project description:Despite a library full of literature on miRNA biology, core issues relating to miRNA target detection, biological effect, and mode of action remain controversial. This essay proposes that the predominant mechanism of direct miRNA action is translational inhibition, whereas the bulk of miRNA effects are mRNA based. It explores several issues confounding miRNA target detection, and discusses their impact on the dominance of "miRNA seed" dogma and the exploration of non-canonical binding sites. Finally, it makes comparisons between miRNA target prediction and transcription factor binding prediction, and questions the value of characterizing miRNA binding sites based on which miRNA nucleotides are paired with an mRNA.
| S-EPMC4671252 | biostudies-other
Project description:Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning.
| S-EPMC3349722 | biostudies-literature
Project description:Accumulating evidence indicates that microRNAs (miRNAs) can function as oncogenes or tumor suppressor genes by controlling few key targets, which in turn contribute to the pathogenesis of cancer. The identification of cancer-related key miRNA-target interactions remains a challenge. We performed a systematic analysis of known cancer-related key interactions manually curated from published papers based on different aspects including sequence, expression and function. Known cancer-related key interactions show more miRNA binding sites (especially for 8mer binding sites), more reliable binding of miRNA to the target region, higher expression associations and broader functional coverage when compared to non-disease-related interactions. Through integrating these sequence, expression and function features, we proposed a bioinformatics approach termed PCmtI to prioritize cancer-related key interactions. Ten-fold cross-validation of our approach revealed that it can achieve an area under the receiver operating characteristic curve of 93.9%. Subsequent leave-one-miRNA-out cross-validation also demonstrated the performance of our approach. Using miR-155 as a case, we found that the top ranked interactions can account for most functions of miR-155. In addition, we further demonstrated the power of our approach by 23 recently identified cancer-related key interactions. The approach described here offers a new way for the discovery of novel cancer-related key miRNA-target interactions.
| S-EPMC3439920 | biostudies-literature